CORRELATION, REGRESSION AND MULTIPLE REGRESSION
What is the purpose of using correlation, regression and multiple regression analysis? How may
these techniques be used in business decisions or in relation to strategy formulation and
implementation? Please provide examples to show your reasoning.
If two quantities vary in such a way that movements in one are accompanied by movements
in the other, these quantities are correlated. The degree of relationship between the variables under
consideration is measured through correlation analysis. The measure of correlation called
correlation coefficient summarizes in one figure the direction and degree of correlation.
The study of correlation is of immense use in practical life because of the following reasons.
1. Most of the variables show some kind of relationship. For example, there is a relationship
between price and supply, income and expenditure etc. with the help of correlation analysis
we can measure in one figure the degree of relationship between the variables
2. Once we know that two variables are closely related, we can estimate the value of one
variable given the value of the other. This done using regression analysis.
3. Correlation analysis contributes to the understanding of economic behaviour, aids in
locating the critically important variables on which others depend, may reveal to the
economist the connection by which disturbances spread and suggest to him the path
through which stabilizing forces mat become effective.
In business, correlation analysis enables the executive to estimate costs, sales, prices and
other variables on the basis of some other series with which these costs, sales and prices
may be functionally related.
4. Progressive development in the methods of science and philosophy has been characterised
by increase in the knowledge of relationship or correlations. In nature also one finds
multiplicity of interrelated forces.
5. The effect of correlation is to reduce the range of uncertainty. The prediction based on
correlation analysis is likely to be more reliable and near to reality.
After having been established the fact that two variables are closely related, we may be
interested in estimating or predicting the value of one variable given the value of another. For
example, if we know that advertising and sales are correlated we may find out expected amount
of sales for a given advertising expenditure or the required amount of advertising expenditure
for attaining a given amount of sales. Regression analysis reveals average relationship between
two variables and this makes possible estimation and prediction.
In nature, relationship tends to be complex rather than simple. One variable is related to a
great number of others, many of which may be interrelated among them.
Multiple regression analysis is represents an extension of two variable regression analysis.
Instead of a single independent variable, two or more independent variables are used to
estimate the values of a dependant variable. The following are three general purposes of
multiple regression and correlation analysis:
1. To derive an equation which provides estimates of the dependent variable from the
values of two or more independent variables.
2. To obtain a measure of the error involved in using this regression equation as a basis for
estimation.
3. To obtain a measure of the proportion of variance in the dependant variable explained
by the independent variables.